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143 lines
5.3 KiB
143 lines
5.3 KiB
## swarms Package Documentation
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Welcome to the documentation for the "swarms" package, designed to facilitate seamless integration with various AI language models and APIs. This package empowers developers, end-users, and system administrators to interact with AI models from different providers, such as OpenAI, Hugging Face, Google PaLM, and Anthropic.
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### Table of Contents
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1. [OpenAI](#openai)
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2. [HuggingFace](#huggingface)
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3. [Google PaLM](#google-palm)
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4. [Anthropic](#anthropic)
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### 1. OpenAI (swarms.OpenAI)
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The OpenAI class provides an interface to interact with OpenAI's language models. It allows both synchronous and asynchronous interactions.
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**Constructor:**
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```python
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OpenAI(api_key: str, system: str = None, console: bool = True, model: str = None, params: dict = None, save_messages: bool = True)
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```
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**Attributes:**
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- `api_key` (str): Your OpenAI API key.
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- `system` (str, optional): A system message to be used in conversations.
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- `console` (bool, default=True): Display console logs.
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- `model` (str, optional): Name of the language model to use.
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- `params` (dict, optional): Additional parameters for model interactions.
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- `save_messages` (bool, default=True): Save conversation messages.
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**Methods:**
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- `generate(message: str, **kwargs) -> str`: Generate a response using the OpenAI model.
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- `generate_async(message: str, **kwargs) -> str`: Generate a response asynchronously.
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- `ask_multiple(ids: List[str], question_template: str) -> List[str]`: Query multiple IDs simultaneously.
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- `stream_multiple(ids: List[str], question_template: str) -> List[str]`: Stream multiple responses.
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**Usage Example:**
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```python
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from swarms import OpenAI
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import asyncio
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chat = OpenAI(api_key="YOUR_OPENAI_API_KEY")
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response = chat.generate("Hello, how can I assist you?")
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print(response)
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ids = ["id1", "id2", "id3"]
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async_responses = asyncio.run(chat.ask_multiple(ids, "How is {id}?"))
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print(async_responses)
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```
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### 2. HuggingFace (swarms.HuggingFaceLLM)
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The HuggingFaceLLM class allows interaction with language models from Hugging Face.
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**Constructor:**
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```python
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HuggingFaceLLM(model_id: str, device: str = None, max_length: int = 20, quantize: bool = False, quantization_config: dict = None)
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```
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**Attributes:**
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- `model_id` (str): ID or name of the Hugging Face model.
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- `device` (str, optional): Device to run the model on (e.g., 'cuda', 'cpu').
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- `max_length` (int, default=20): Maximum length of generated text.
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- `quantize` (bool, default=False): Apply model quantization.
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- `quantization_config` (dict, optional): Configuration for quantization.
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**Methods:**
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- `generate(prompt_text: str, max_length: int = None) -> str`: Generate text based on a prompt.
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**Usage Example:**
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```python
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from swarms import HuggingFaceLLM
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model_id = "gpt2"
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hugging_face_model = HuggingFaceLLM(model_id=model_id)
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prompt = "Once upon a time"
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generated_text = hugging_face_model.generate(prompt)
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print(generated_text)
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```
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### 3. Google PaLM (swarms.GooglePalm)
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The GooglePalm class provides an interface for Google's PaLM Chat API.
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**Constructor:**
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```python
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GooglePalm(model_name: str = "models/chat-bison-001", google_api_key: str = None, temperature: float = None, top_p: float = None, top_k: int = None, n: int = 1)
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```
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**Attributes:**
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- `model_name` (str): Name of the Google PaLM model.
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- `google_api_key` (str, optional): Google API key.
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- `temperature` (float, optional): Temperature for text generation.
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- `top_p` (float, optional): Top-p sampling value.
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- `top_k` (int, optional): Top-k sampling value.
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- `n` (int, default=1): Number of candidate completions.
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**Methods:**
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- `generate(messages: List[Dict[str, Any]], stop: List[str] = None, **kwargs) -> Dict[str, Any]`: Generate text based on a list of messages.
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- `__call__(messages: List[Dict[str, Any]], stop: List[str] = None, **kwargs) -> Dict[str, Any]`: Generate text using the call syntax.
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**Usage Example:**
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```python
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from swarms import GooglePalm
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google_palm = GooglePalm()
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messages = [{"role": "system", "content": "You are a helpful assistant"}, {"role": "user", "content": "Tell me a joke"}]
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response = google_palm.generate(messages)
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print(response["choices"][0]["text"])
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```
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### 4. Anthropic (swarms.Anthropic)
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The Anthropic class enables interaction with Anthropic's large language models.
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**Constructor:**
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```python
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Anthropic(model: str = "claude-2", max_tokens_to_sample: int = 256, temperature: float = None, top_k: int = None, top_p: float = None, streaming: bool = False, default_request_timeout: int = None)
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```
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**Attributes:**
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- `model` (str): Name of the Anthropic model.
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- `max_tokens_to_sample` (int, default=256): Maximum tokens to sample.
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- `temperature` (float, optional): Temperature for text generation.
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- `top_k` (int, optional): Top-k sampling value.
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- `top_p` (float, optional): Top-p sampling value.
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- `streaming` (bool, default=False): Enable streaming mode.
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- `default_request_timeout` (int, optional): Default request timeout.
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**Methods:**
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- `generate(prompt: str, stop: List[str] = None) -> str`: Generate text based on a prompt.
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**Usage Example:**
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```python
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from swarms import Anthropic
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anthropic = Anthropic()
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prompt = "Once upon a time"
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generated_text = anthropic.generate(prompt)
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print(generated_text)
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```
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This concludes the documentation for the "swarms" package, providing you with tools to seamlessly integrate with various language models and APIs. Happy coding! |